Adaptive Learning for Dynamic Features and Noisy Labels

Applying current machine learning algorithms in complex and open environments remains challenging, especially when different changing elements are coupled and the training data is scarce. For example, in the activity recognition task, the motion sensors may change position or fall off due to the int...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 47; H. 2; S. 1219 - 1237
Hauptverfasser: Gu, Shilin, Xu, Chao, Hu, Dewen, Hou, Chenping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.02.2025
Schlagworte:
Adaptation models
Adaptive learning
Data models
Dynamic feature space
Feature extraction
heterogeneous model reuse
Heuristic algorithms
incremental learning
Noise
Noise measurement
noisy labels
Risk minimization
Streams
Training
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Zusammenfassung:Applying current machine learning algorithms in complex and open environments remains challenging, especially when different changing elements are coupled and the training data is scarce. For example, in the activity recognition task, the motion sensors may change position or fall off due to the intensity of the activity, leading to changes in feature space and finally resulting in label noise. Learning from such a problem where the dynamic features are coupled with noisy labels is crucial but rarely studied, particularly when the noisy samples in new feature space are limited. In this paper, we tackle the above problem by proposing a novel two-stage algorithm, called Adaptive Learning for Dynamic features and Noisy labels (ALDN). Specifically, optimal transport is first modified to map the previously learned heterogeneous model to the prior model of the current stage. Then, to fully reuse the mapped prior model, we add a simple yet efficient regularizer as the consistency constraint to assist both the estimation of the noise transition matrix and the model training in the current stage. Finally, two implementations with direct (ALDN-D) and indirect (ALDN-ID) constraints are illustrated for better investigation. More importantly, we provide theoretical guarantees for risk minimization of ALDN-D and ALDN-ID. Extensive experiments validate the effectiveness of the proposed algorithms.
Bibliographie:ObjectType-Article-1
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2024.3489217